Density functional theory (DFT) based energy and electronic structure calculations have been widely used as a reliable and powerful tool to efficiently understand catalytic reactions of various materials. However, when it comes to nanostructured catalytic materials having heterogeneity, complex morphologies, or photoreactivity (especially for wide band gap semiconductors), merely using DFT calculations cannot give reliable answers. For last years I and my research group have developed theory models and multi-scale simulation platform to advance the predictions of the catalytic activities of nanomaterials having incoherent interfaces and largely changed activity by point defects. In this talk, I will present two topics: 1) the validity of using Planck-Nernst-Poisson model in multi-scale simulations for investigations on interface polarization effects, 2) How to deal with photoactive wide band gap materials in predictions of their catalytic reactivities, combining mathematical models and simple DFT energy calculations.

Heechae Choi is a full Professor at Department of Chemistry and Materials Science, School of Science, Xi’an Jiaotong-Liverpool University (XJTLU) since January 2023. Before joining XJTLU, he had his first independent academic career as a Group Leader (Principal Investigator) at Institute of Inorganic and Materials Chemistry, University of Cologne, Germany, after receiving a top-level grant, MOPGA-GRI fellowship from 2018 to 2022. He received his Bachelor and Doctoral degrees both from Hanyang University in 2006 and 2012, respectively. Before he started academic career as faculty, he was trained in Korea Institute of Science Technology (KIST) for 4 years, and founded two venture companies, Virtual Lab Inc. and Materials Lab (Mar.2016, Aug.2018), which provide materials simulation platform and consulting services to big manufacturing companies including Samsung Electronics, 3M, SK, MiCo, respectively. Heechae’s most important and representative research achievements are (1) theory model development for semiconductor etching process, (2) statistical mechanics-based nanoparticle shape control for electronic devices and diamond processing, (3) Proof of the validity of classical Poisson-Nernst-Planck model in ion transport behavior predictions at electrocatalyst surfaces using Machine Learning, and (4) demonstrating the amorphous-crystal junctioned photocatalyst system as the first time.